Machine Learning Combined with Restriction Enzyme Mining Assists in the Design of Multi-Point Mutagenic Primers

Abstract

The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories use it to perform all kinds of single nucleotide polymorphisms (SNPs) genotyping works, and as a molecular biotechnology for disease-related analysis. However, many single nucleotide polymorphisms lack available restriction enzymes to distinguish the specific genotypes on a target SNP, and that causes the PCR-RFLP assay which is unavailable to be called mismatch PCR-RFLP. In order to completely solve the problem of mismatch PCR-RFLP, we have created a teaching–learning-based optimization (TLBO) multi-point mutagenic primer design algorithm which, combined with REHUNT, provides a complete and specific restriction enzyme mining solution. The proposed method not only introduces several search strategies suitable for multi-point mutagenesis primers, but also enhances the reliability of mutagenic primer design. In addition, this study is also designed for more complex SNP structures (with multiple dNTPs and insertion and deletion) to provide specific solutions for SNP diversity. We tested against fifteen mismatch PCR-RFLP SNPs in the human SLC6A4 gene on the NCBI dbSNP database as experimental templates. The experimental results indicate that the proposed method is helpful for providing the multi-point mutagenic primers that meet the constrain conditions of PCR experiments.